The linear scheduling method (LSM) for optimization in linear projects has been the focus of numerous academic studies over the years. However, research on incorporating reverse construction activities and other practical scenarios, such as flexible activity–section–crew assignment demands, in linear project-scheduling optimization remains insufficient. This has further spurred research on practical scenario-based linear project-scheduling optimization. We performed an analysis of a description method system within the LSM framework, outlining the spatiotemporal logical relationship in scenarios involving reverse activities. A scheduling optimization model incorporating a flexible constraint system, capable of handling multisection–multicrew, multicrew–multimode, and multicrew–multiconstruction direction scenarios is developed to enhance the practicability of linear project-scheduling optimization. Additionally, an advanced grey wolf optimization (GWO) algorithm is devised and validated through a real-world case study. The case study provides insights into the impact of reverse construction activities on the linear project-scheduling problem, specifically in the dimension of project duration.

Line planning and timetabling play important roles in the design of urban rail transportation services. Due to the complexity of the integrated optimization of entire transportation plans, previous studies have generally considered line planning and timetabling design independently, which cannot ensure the global optimality of transportation services. In this study, the integrated design problem of line planning and timetabling was characterized as an equilibrium space–time network design problem and solved with a bi-objective nonlinear integer programming model. The model, in which train overtaking and passenger path choice behavior were considered, adjusted the network topology and link attributes (time and capacity) of the travel space–time network by optimizing the train service frequency, operation zone, stopping pattern, train formation, and train order to minimize the system life cycle cost and total passenger travel time perception. An algorithm was constructed using the non-dominated sorting genetic algorithm II combined with the self-adaptive gradient projection algorithm to solve the model. A real-world case was considered to evaluate the effectiveness of the proposed model and algorithm. The results showed that the model not only performed well in the trade-off between system cost and passenger travel efficiency, but it could also reduce the imbalance of train and station loads. Pareto front analysis of the model with different parameters showed that more types of trains did not correlate with a better performance, some line-planning strategies had a combination effect, and multi-strategy line planning was more suitable for scenarios with a high imbalance in the temporal and spatial distributions of passenger flow.